app.py

import gradio as gr

import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor, pipeline
import pandas as pd
from sklearn.model_selection import train_test_split

from noisereduce.torchgate import TorchGate as TG
import re
from pydub import AudioSegment



# processor = Wav2Vec2Processor.from_pretrained("RikeshSilwal/wav2vec2-nepali")
# model = Wav2Vec2ForCTC.from_pretrained("RikeshSilwal/wav2vec2-nepali")

processor = Wav2Vec2Processor.from_pretrained("RikeshSilwal/wav2vec2-nepali-rikeshsilwal")
model = Wav2Vec2ForCTC.from_pretrained("RikeshSilwal/wav2vec2-nepali-rikeshsilwal")

from torchaudio.transforms import Resample
import numpy as np





# def transcribe_audio(audio_file):
#   input_arr, sampling_rate =torchaudio.load(audio_file)
#   resampler = Resample(orig_freq=sampling_rate, new_freq=16000)
#   input_arr = resampler(input_arr).squeeze().numpy()
#   sampling_rate = 16000
#   inputs = processor(input_arr, sampling_rate=16_000, return_tensors="pt", padding=True)

#   with torch.no_grad():
#     logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits

#   predicted_ids = torch.argmax(logits, dim=-1)

#   predicted_words=  processor.batch_decode(predicted_ids)

#   return predicted_words[0]

def transcribe_audio(audio_file):
    audio = AudioSegment.from_wav(audio_file)

    device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

    input_arr, sampling_rate =torchaudio.load(audio_file)
    # Create TorchGating instance
    tg = TG(sr=sampling_rate, nonstationary=True).to(device)
    try:
      input_arr = tg(input_arr)
    except:
      input_arr = input_arr
    if sampling_rate != 16000:
      resampler = torchaudio.transforms.Resample(orig_freq=sampling_rate, new_freq=16000)
      input_arr = resampler(input_arr).squeeze().numpy()

    recognizer = pipeline("automatic-speech-recognition", model="Harveenchadha/vakyansh-wav2vec2-nepali-nem-130")
    prediction = recognizer(input_arr, chunk_length_s=5, stride_length_s=(2,1))
    prediction = recognizer(input_arr)
    prediction = re.sub('[<s>]' , '' , str(prediction['text']))

audio_input = gr.inputs.Audio(source="upload", type="filepath")

iface = gr.Interface(fn=transcribe_audio, inputs=audio_input,
                         outputs=["textbox"], title="Speech To Text",
                         description="Upload an audio file and hit the 'Submit'\
                             button")

iface.launch(inline=False)